Using speech recognition results based on an unstructured language model with a music system

ABSTRACT

Speech recorded by an audio capture facility of a music facility is processed by a speech recognition facility to generate results that are provided to the music facility. When information related to a music application running on the music facility are provided to the speech recognition facility, the results generated are based at least in part on the application related information. The speech recognition facility uses an unstructured language model for generating results. The user of the music facility may optionally be allowed to edit the results being provided to the music facility. The speech recognition facility may also adapt speech recognition based on usage of the results.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the following provisionalapplications, each of which is hereby incorporated by reference in itsentirety: U.S. Provisional App. Ser. No. 60976050 filed Sep. 28, 2007;U.S. Provisional App. Ser. No. 60977143 filed Oct. 3, 2007; and U.S.Provisional App. Ser. No. 61034794 filed Mar. 7, 2008.

This application is a continuation-in-part of the following U.S. patentapplications, each of which is incorporated by reference in itsentirety: U.S. patent application Ser. No. 11/865,692 filed Oct. 1,2007; U.S. patent application Ser. No. 11/865,694 filed Oct. 1, 2007;U.S. patent application Ser. No. 11/865,697 filed Oct. 1, 2007; U.S.patent application Ser. No. 11/866,675 filed Oct. 3, 2007; U.S. patentapplication Ser. No. 11/866,704 filed Oct. 3, 2007; U.S. patentapplication Ser. No. 11/866,725 filed Oct. 3, 2007; U.S. patentapplication Ser. No. 11/866,755 filed Oct. 3, 2007; U.S. patentapplication Ser. No. 11/866,777 filed Oct. 3, 2007; U.S. patentapplication Ser. No. 11/866,804 filed Oct. 3, 2007; U.S. patentapplication Ser. No. 11/866,818 filed Oct. 3, 2007; and U.S. patentapplication Ser. No. 12/044,573 filed Mar. 7, 2008 which claims thebenefit of U.S. Provisional App. Ser. No. 60893600 filed Mar. 7, 2007.

This application is a continuation of U.S. patent application Ser. No.12/123,952 filed May 20, 2008.

This application claims priority to international patent applicationSer. No. PCTUS2008056242 filed Mar. 7, 2008.

BACKGROUND

Field:

The present invention is related to speech recognition, and specificallyto speech recognition in association with a mobile communicationsfacility or a device which provides a service to a user such as a musicplaying device or a navigation system.

Description of the Related Art:

Speech recognition, also known as automatic speech recognition, is theprocess of converting a speech signal to a sequence of words by means ofan algorithm implemented as a computer program. Speech recognitionapplications that have emerged in recent years include voice dialing(e.g., call home), call routing (e.g., I would like to make a collectcall), simple data entry (e.g., entering a credit card number), andpreparation of structured documents (e.g., a radiology report). Currentsystems are either not for mobile communication devices or utilizeconstraints, such as requiring a specified grammar, to provide real-timespeech recognition.

SUMMARY

The current invention provides a facility for unconstrained, mobile ordevice-based, real-time speech recognition. The current invention allowsan individual with a mobile communications facility to use speechrecognition to enter text, such as into a communications application,such as an SMS message, instant messenger, e-mail, or any otherapplication, such as applications for getting directions, entering aquery word string into a search engine, commands into a navigation ormap program, and a wide range of other text entry applications. Inaddition, the current invention allows users to interact with a widerange of devices, such music players or navigation systems, to perform avariety of tasks (e.g. choosing a song, entering a destination, and thelike). These devices may be specialized devices for performing such afunction, or may be general purpose computing, entertainment, orinformation devices that interact with the user to perform some functionfor the user.

In embodiments the present invention may provide for the entering oftext into a software application resident on a mobile communicationfacility, where recorded speech may be presented by the user using themobile communications facility's resident capture facility. Transmissionof the recording may be provided through a wireless communicationfacility to a speech recognition facility, and may be accompanied byinformation related to the software application. Results may begenerated utilizing the speech recognition facility that may beindependent of structured grammar, and may be based at least in part onthe information relating to the software application and the recording.The results may then be transmitted to the mobile communicationsfacility, where they may be loaded into the software application. Inembodiments, the user may be allowed to alter the results that arereceived from the speech recognition facility. In addition, the speechrecognition facility may be adapted based on usage.

In embodiments, the information relating to the software application mayinclude at least one of an identity of the application, an identity of atext box within the application, contextual information within theapplication, an identity of the mobile communication facility, anidentity of the user, and the like.

In embodiments, the step of generating the results may be based at leastin part on the information relating to the software application involvedin selecting at least one of a plurality of recognition models based onthe information relating to the software application and the recording,where the recognition models may include at least one of an acousticmodel, a pronunciation, a vocabulary, a language model, and the like,and at least one of a plurality of language models, wherein the at leastone of the plurality of language models may be selected based on theinformation relating to the software application and the recording. Inembodiments, the plurality of language models may be run at the sametime or in multiple passes in the speech recognition facility. Theselection of language models for subsequent passes may be based on theresults obtained in previous passes. The output of multiple passes maybe combined into a single result by choosing the highest scoring result,the results of multiple passes, and the like, where the merging ofresults may be at the word, phrase, or the like level.

In embodiments, adapting the speech recognition facility may be based onusage that includes at least one of adapting an acoustic model, adaptinga pronunciation, adapting a vocabulary, adapting a language model, andthe like. Adapting the speech recognition facility may include adaptingrecognition models based on usage data, where the process may be anautomated process, the models may make use of the recording, the modelsmay make use of words that are recognized, the models may make use ofthe information relating to the software application about action takenby the user, the models may be specific to the user or groups of users,the models may be specific to text fields with in the softwareapplication or groups of text fields within the software applications,and the like.

In embodiments, the step of allowing the user to alter the results mayinclude the user editing a text result using at least one of a keypad ora screen-based text correction mechanism, selecting from among aplurality of alternate choices of words contained in the results,selecting from among a plurality of alternate actions related to theresults, selecting among a plurality of alternate choices of phrasescontained in the results, selecting words or phrases to alter byspeaking or typing, positioning a cursor and inserting text at thecursor position by speaking or typing, and the like. In addition, thespeech recognition facility may include a plurality of recognitionmodels that may be adapted based on usage, including utilizing resultsaltered by the user, adapting language models based on usage fromresults altered by the user, and the like.

In embodiments, the present invention may provide this functionalityacross application on a mobile communication facility. So, it may bepresent in more than one software application running on the mobilecommunication facility. In addition, the speech recognitionfunctionality may be used to not only provide text to applications butmay be used to decide on an appropriate action for a user's query andtake that action either by performing the action directly, or byinvoking an application on the mobile communication facility andproviding that application with information related to what the userspoke so that the invoked application may perform the action taking intoaccount the spoken information provided by the user.

In embodiments, the speech recognition facility may also tag the outputaccording to type or meaning of words or word strings and pass thistagging information to the application. Additionally, the speechrecognition facility may make use of human transcription input toprovide real-term input to the overall system for improved performance.This augmentation by humans may be done in a way which is largelytransparent to the end-user.

In embodiments, the present invention may provide all of thisfunctionality to a wide range of devices including special purposedevices such as music players, personal navigation systems, set-topboxes, digital video recorders, in-car devices, and the like. It mayalso be used in more general purpose computing, entertainment,information, and communication devices.

The system components including the speech recognition facility, userdatabase, content database, and the like may be distributed across anetwork or in some implementations may be resident on the device itself,or may be a combination of resident and distributed components. Based onthe configuration, the system components may be loosely coupled throughwell-defined communication protocols and APIs or may be tightly tied tothe applications or services on the device.

A method and system of entering text into a music system is provided.The method and system may include recording speech presented by a userusing a resident capture facility, providing the recording to a speechrecognition facility, generating results utilizing the speechrecognition facility using an unstructured language model based at leastin part on the information relating to the recording, and using theresults in the music system.

In embodiments, using user feedback may adapt the unstructured languagemodel.

In embodiments, the speech recognition facility may be remotely locatedfrom the music system. The music system may provide information relatingto the music application to the speech recognition facility and thegenerating results is based at least in part on this information.

The information relating to the music application may include at leastone of an identity of the application, an identity of a text box withinthe application, contextual information within the application, anidentity of the music system, and an identity of the user. Thecontextual information may include at least one of the usage history ofthe music application, information from a user's favorites list orplaylists, information about music currently stored on the music system,and information currently displayed in the music application.

In embodiments, the step of generating the results based at least inpart on the information relating to the music application involvesselecting at least one of a plurality of recognition models based on theinformation relating to the music application and the recording. Thespeech recognition facility may select at least one language model basedat least in part on the information relating to music system. The atleast one selected language model may be at least one of a generallanguage model for artists, a general language models for song titles,and a general language model for music types. The at least one selectedlanguage model may be based on an estimate of the type of music the useris interested in.

A method and system of entering text into music system is provided. Themethod and system may include recording speech presented by a user usinga resident capture facility, providing the recording to a speechrecognition facility, generating results utilizing the speechrecognition facility using an unstructured language model based at leastin part on the information relating to the recording, using the resultsin the music system and adapting the speech recognition facility basedon usage.

In embodiments, the speech recognition facility may be remotely locatedfrom the music system. In embodiments, adapting the speech recognitionfacility may be based on usage includes at least one of adapting anacoustic model, adapting a set of pronunciations, adapting a vocabulary,and adapting a language model. Adapting the speech recognition facilitymay include adapting recognition models based on usage data. Adaptingrecognition models may make use of the information from the music systemabout actions taken by the user. Adapting recognition models may bespecific to the music system. Adapting recognition models may bespecific to text fields within the music application running on themusic system or groups of text fields within the music application.

In embodiments, the music system may provide information relating to themusic application running on the music system to the speech recognitionfacility and the generating results is based at least in part on thisinformation. The information may relate to the music applicationincludes at least one of an identity of the application, an identity ofa text box within the application, contextual information within theapplication, an identity of the music system, and an identity of theuser.

In embodiments, the step of generating the results based at least inpart on the information relating to the music application involvesselecting at least one of a plurality of recognition models based on theinformation relating to the music application and the recording.

A method and system of entering text into a music system is provided.The method and system may include recording speech presented by a userusing a resident capture facility, providing the recording to a speechrecognition facility, generating results utilizing the speechrecognition facility using an unstructured language model based at leastin part on the information relating to the recording, allowing the userto alter the results and using the results in the music system.

In embodiments, the speech recognition facility may be remotely locatedfrom the music system. The music system may provide information relatingto the music application running on the music system to the speechrecognition facility and the generating results is based at least inpart on music related information.

In embodiments, the step of allowing the user to alter the results mayinclude the user editing a text result using at least one of a set ofbutton or other controls, and a screen-based text correction mechanismon the music system.

In embodiments, the step of allowing the user to alter the results mayinclude the user selecting from among a plurality of alternate choicesof words contained in the results from the speech recognition facility.

In embodiments, the step of allowing the user to alter the resultsincludes the user selecting from among a plurality of alternate actionsrelated to the results from the speech recognition facility.

In embodiments, the step of allowing the user to alter the results mayinclude the user selecting words or phrases to alter by speaking ortyping.

In embodiments, the step of allowing the user to alter the resultsincludes the user selecting words or phrases to alter by speaking ortyping.

These and other systems, methods, objects, features, and advantages ofthe present invention will be apparent to those skilled in the art fromthe following detailed description of the preferred embodiment and thedrawings. All documents mentioned herein are hereby incorporated intheir entirety by reference.

BRIEF DESCRIPTION OF THE FIGURES

The invention and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1 depicts a block diagram of the mobile environment speechprocessing facility.

FIG. 1A depicts a block diagram of a music system.

FIG. 1B depicts a block diagram of a navigation system.

FIG. 1C depicts a block diagram of a mobile communications facility.

FIG. 2 depicts a block diagram of the automatic speech recognitionserver infrastructure architecture.

FIG. 2A depicts a block diagram of the automatic speech recognitionserver infrastructure architecture including a component for taggingwords.

FIG. 2B depicts a block diagram of the automatic speech recognitionserver infrastructure architecture including a component for real timehuman transcription.

FIG. 3 depicts a block diagram of the application infrastructurearchitecture.

FIG. 4 depicts some of the components of the ASR Client.

FIG. 5A depicts the process by which multiple language models may beused by the ASR engine.

FIG. 5B depicts the process by which multiple language models may beused by the ASR engine for a navigation application embodiment.

FIG. 5C depicts the process by which multiple language models may beused by the ASR engine for a messaging application embodiment.

FIG. 5D depicts the process by which multiple language models may beused by the ASR engine for a content search application embodiment.

FIG. 5E depicts the process by which multiple language models may beused by the ASR engine for a search application embodiment.

FIG. 5F depicts the process by which multiple language models may beused by the ASR engine for a browser application embodiment.

FIG. 6 depicts the components of the ASR engine.

FIG. 7 depicts the layout and initial screen for the user interface.

FIG. 7A depicts the flow chart for determining application levelactions.

FIG. 7B depicts a searching landing page.

FIG. 7C depicts a SMS text landing page

FIG. 8 depicts a keypad layout for the user interface.

FIG. 9 depicts text boxes for the user interface.

FIG. 10 depicts a first example of text entry for the user interface.

FIG. 11 depicts a second example of text entry for the user interface.

FIG. 12 depicts a third example of text entry for the user interface.

FIG. 13 depicts speech entry for the user interface.

FIG. 14 depicts speech-result correction for the user interface.

FIG. 15 depicts a first example of navigating browser screen for theuser interface.

FIG. 16 depicts a second example of navigating browser screen for theuser interface.

FIG. 17 depicts packet types communicated between the client, router,and server at initialization and during a recognition cycle.

FIG. 18 depicts an example of the contents of a header.

FIG. 19 depicts the format of a status packet.

DETAILED DESCRIPTION

The current invention may provide an unconstrained, real-time, mobileenvironment speech processing facility 100, as shown in FIG. 1, thatallows a user with a mobile communications facility 120 to use speechrecognition to enter text into an application 112, such as acommunications application, an SMS message, IM message, e-mail, chat,blog, or the like, or any other kind of application, such as a socialnetwork application, mapping application, application for obtainingdirections, search engine, auction application, application related tomusic, travel, games, or other digital media, enterprise softwareapplications, word processing, presentation software, and the like. Invarious embodiments, text obtained through the speech recognitionfacility described herein may be entered into any application orenvironment that takes text input.

In an embodiment of the invention, the user's 130 mobile communicationsfacility 120 may be a mobile phone, programmable through a standardprogramming language, such as Java, C, Brew, C++, and any other currentor future programming language suitable for mobile device applications,software, or functionality. The mobile environment speech processingfacility 100 may include a mobile communications facility 120 that ispreloaded with one or more applications 112. Whether an application 112is preloaded or not, the user 130 may download an application 112 to themobile communications facility 120. The application 112 may be anavigation application, a music player, a music download service, amessaging application such as SMS or email, a video player or searchapplication, a local search application, a mobile search application, ageneral internet browser, or the like. There may also be multipleapplications 112 loaded on the mobile communications facility 120 at thesame time. The user 130 may activate the mobile environment speechprocessing facility's 100 user interface software by starting a programincluded in the mobile environment speech processing facility 120 oractivate it by performing a user 130 action, such as pushing a button ora touch screen to collect audio into a domain application. The audiosignal may then be recorded and routed over a network to servers 110 ofthe mobile environment speech processing facility 100. Text, which mayrepresent the user's 130 spoken words, may be output from the servers110 and routed back to the user's 130 mobile communications facility120, such as for display. In embodiments, the user 130 may receivefeedback from the mobile environment speech processing facility 100 onthe quality of the audio signal, for example, whether the audio signalhas the right amplitude; whether the audio signal's amplitude isclipped, such as clipped at the beginning or at the end; whether thesignal was too noisy; or the like.

The user 130 may correct the returned text with the mobile phone'skeypad or touch screen navigation buttons. This process may occur inreal-time, creating an environment where a mix of speaking and typing isenabled in combination with other elements on the display. The correctedtext may be routed back to the servers 110, where an Automated SpeechRecognition (ASR) Server infrastructure 102 may use the corrections tohelp model how a user 130 typically speaks, what words are used, how theuser 130 tends to use words, in what contexts the user 130 speaks, andthe like. The user 130 may speak or type into text boxes, withkeystrokes routed back to the ASR server infrastructure 102.

In addition, the hosted servers 110 may be run as an application serviceprovider (ASP). This may allow the benefit of running data from multipleapplications 112 and users 130, combining them to make more effectiverecognition models. This may allow usage based adaptation of speechrecognition to the user 130, to the scenario, and to the application112.

One of the applications 112 may be a navigation application whichprovides the user 130 one or more of maps, directions, businesssearches, and the like. The navigation application may make use of a GPSunit in the mobile communications facility 120 or other means todetermine the current location of the mobile communications facility120. The location information may be used both by the mobile environmentspeech processing facility 100 to predict what users may speak, and maybe used to provide better location searches, maps, or directions to theuser. The navigation application may use the mobile environment speechprocessing facility 100 to allow users 130 to enter addresses, businessnames, search queries and the like by speaking.

Another application 112 may be a messaging application which allows theuser 130 to send and receive messages as text via Email, SMS, IM, or thelike to and from other people. The messaging application may use themobile environment speech processing facility 100 to allow users 130 tospeak messages which are then turned into text to be sent via theexisting text channel.

Another application 112 may be a music application which allows the user130 to play music, search for locally stored content, search for anddownload and purchase content from network-side resources and the like.The music application may use the mobile environment speech processingfacility 100 to allow users 130 to speak song title, artist names, musiccategories, and the like which may be used to search for music contentlocally or in the network, or may allow users 130 to speak commands tocontrol the functionality of the music application.

Another application 112 may be a content search application which allowsthe user 130 to search for music, video, games, and the like. Thecontent search application may use the mobile environment speechprocessing facility 100 to allow users 130 to speak song or artistnames, music categories, video titles, game titles, and the like whichmay be used to search for content locally or in the network

Another application 112 may be a local search application which allowsthe user 130 to search for business, addresses, and the like. The localsearch application may make use of a GPS unit in the mobilecommunications facility 120 or other means to determine the currentlocation of the mobile communications facility 120. The current locationinformation may be used both by the mobile environment speech processingfacility 100 to predict what users may speak, and may be used to providebetter location searches, maps, or directions to the user. The localsearch application may use the mobile environment speech processingfacility 100 to allow users 130 to enter addresses, business names,search queries and the like by speaking.

Another application 112 may be a general search application which allowsthe user 130 to search for information and content from sources such asthe World Wide Web. The general search application may use the mobileenvironment speech processing facility 100 to allow users 130 to speakarbitrary search queries.

Another application 112 may be a browser application which allows theuser 130 to display and interact with arbitrary content from sourcessuch as the World Wide Web. This browser application may have the fullor a subset of the functionality of a web browser found on a desktop orlaptop computer or may be optimized for a mobile environment. Thebrowser application may use the mobile environment speech processingfacility 100 to allow users 130 to enter web addresses, control thebrowser, select hyperlinks, or fill in text boxes on web pages byspeaking.

In an embodiment, the speech recognition facility 142 may be built intoa device such as a music device 140 or a navigation system 150. In thiscase, the speech recognition facility allows users to enter informationsuch as a song or artist name or a navigation destination into thedevice.

FIG. 1 depicts an architectural block diagram for the mobile environmentspeech processing facility 100, including a mobile communicationsfacility 120 and hosted servers 110 The ASR client may provide thefunctionality of speech-enabled text entry to the application. The ASRserver infrastructure 102 may interface with the ASR client 118, in theuser's 130 mobile communications facility 120, via a data protocol, suchas a transmission control protocol (TCP) connection or the like. The ASRserver infrastructure 102 may also interface with the user database 104.The user database 104 may also be connected with the registration 108facility. The ASR server infrastructure 102 may make use of externalinformation sources 124 to provide information about words, sentences,and phrases that the user 130 is likely to speak. The application 112 inthe user's mobile communication facility 120 may also make use ofserver-side application infrastructure 122, also via a data protocol.The server-side application infrastructure 122 may provide content forthe applications, such as navigation information, music or videos todownload, search facilities for content, local, or general web search,and the like. The server-side application infrastructure 122 may alsoprovide general capabilities to the application such as translation ofHTML or other web-based markup into a form which is suitable for theapplication 112. Within the user's 130 mobile communications facility120, application code 114 may interface with the ASR client 118 via aresident software interface, such as Java, C, C++, and the like. Theapplication infrastructure 122 may also interface with the user database104, and with other external application information sources 128 such asthe World Wide Web 330, or with external application-specific contentsuch as navigation services, music, video, search services, and thelike.

FIG. 1A depicts the architecture in the case where the speechrecognition facility 142 as described in various preferred embodimentsdisclosed herein is associated with or built into a music device 140.The application 112 provides functionality for selecting songs, albums,genres, artists, play lists and the like, and allows the user 130 tocontrol a variety of other aspects of the operation of the music playersuch as volume, repeat options, and the like. In an embodiment, theapplication code 114 interacts with the ASR client 118 to allow users toenter information, enter search terms, provide commands by speaking, andthe like. The ASR client 118 interacts with the speech recognitionfacility 142 to recognize the words that the user spoke. There may be adatabase of music content 144 on or available to the device which may beused both by the application code 114 and by the speech recognitionfacility 142. The speech recognition facility 142 may use data ormetadata from the database of music content 144 to influence therecognition models used by the speech recognition facility 142. Theremay be a database of usage history 148 which keeps track of the pastusage of the music system 140. This usage history 148 may include songs,albums, genres, artists, and play lists the user 130 has selected in thepast. In embodiments, the usage history 148 may be used to influence therecognition models used in the speech recognition facility 142. Thisinfluence of the recognition models may include altering the languagemodels to increase the probability that previously requested artists,songs, albums, or other music terms may be recognized in future queries.This may include directly altering the probabilities of terms used inthe past, and may also include altering the probabilities of termsrelated to those used in the past. These related terms may be derivedbased on the structure of the data, for example groupings of artists orother terms based on genre, so that if a user asks for an artist from aparticular genre, the terms associated with other artists in that genremay be altered. Alternatively, these related terms may be derived basedon correlations of usages of terms observed in the past, includingobservations of usage across users. Therefore, it may be learned by thesystem that if a user asks for artist1, they are also likely to askabout artist2 in the future. The influence of the language models basedon usage may also be based on error-reduction criteria. So, not only maythe probabilities of used terms be increased in the language models, butin addition, terms which are misrecognized may be penalized in thelanguage models to decrease their chances of future misrecognitions.

FIG. 1B depicts the architecture in the case where the speechrecognition facility 142 is built into a navigation system 150. Thenavigation system 150 might be an in-vehicle navigation system, apersonal navigation system, or other type of navigation system. Inembodiments the navigation system 150 might, for example, be a personalnavigation system integrated with a mobile phone or other mobilefacility as described throughout this disclosure. The application 112 ofthe navigation system 150 can provide functionality for selectingdestinations, computing routes, drawing maps, displaying points ofinterest, managing favorites and the like, and can allow the user 130 tocontrol a variety of other aspects of the operation of the navigationsystem, such as display modes, playback modes, and the like. Theapplication code 114 interacts with the ASR client 118 to allow users toenter information, destinations, search terms, and the like and toprovide commands by speaking. The ASR client 118 interacts with thespeech recognition facility 142 to recognize the words that the userspoke. There may be a database of navigation-related content 154 on oravailable to the device. Data or metadata from the database ofnavigation-related content 154 may be used both by the application code114 and by the speech recognition facility 142. The navigation contentor metadata may include general information about maps, streets, routes,traffic patterns, points of interest and the like, and may includeinformation specific to the user such as address books, favorites,preferences, default locations, and the like. The speech recognitionfacility 142 may use this navigation content 154 to influence therecognition models used by the speech recognition facility 142. Theremay be a database of usage history 158 which keeps track of the pastusage of the navigation system 150. This usage history 158 may includelocations, search terms, and the like that the user 130 has selected inthe past. The usage history 158 may be used to influence the recognitionmodels used in the speech recognition facility 142. This influence ofthe recognition models may include altering the language models toincrease the probability that previously requested locations, commands,local searches, or other navigation terms may be recognized in futurequeries. This may include directly altering the probabilities of termsused in the past, and may also include altering the probabilities ofterms related to those used in the past. These related terms may bederived based on the structure of the data, for example business names,street names, or the like within particular geographic locations, sothat if a user asks for a destination within a particular geographiclocation, the terms associated with other destinations within thatgeographic location may be altered. Or, these related terms may bederived based on correlations of usages of terms observed in the past,including observations of usage across users. So, it may be learned bythe system that if a user asks for a particular business name they maybe likely to ask for other related business names in the future. Theinfluence of the language models based on usage may also be based onerror-reduction criteria. So, not only may the probabilities of usedterms be increased in the language models, but in addition, terms whichare misrecognized may be penalized in the language models to decreasetheir chances of future misrecognitions.

FIG. 1C depicts the case wherein multiple applications 112, eachinteract with one or more ASR clients 118 and use speech recognitionfacilities 110 to provide speech input to each of the multipleapplications 112. The ASR client 118 may facilitate speech-enabled textentry to each of the multiple applications. The ASR serverinfrastructure 102 may interface with the ASR clients 118 via a dataprotocol, such as a transmission control protocol (TCP) connection,HTTP, or the like. The ASR server infrastructure 102 may also interfacewith the user database 104. The user database 104 may also be connectedwith the registration 108 facility. The ASR server infrastructure 102may make use of external information sources 124 to provide informationabout words, sentences, and phrases that the user 130 is likely tospeak. The applications 112 in the user's mobile communication facility120 may also make use of server-side application infrastructure 122,also via a data protocol. The server-side application infrastructure 122may provide content for the applications, such as navigationinformation, music or videos to download, search facilities for content,local, or general web search, and the like. The server-side applicationinfrastructure 122 may also provide general capabilities to theapplication such as translation of HTML or other web-based markup into aform which is suitable for the application 112. Within the user's 130mobile communications facility 120, application code 114 may interfacewith the ASR client 118 via a resident software interface, such as Java,C, C++, and the like. The application infrastructure 122 may alsointerface with the user database 104, and with other externalapplication information sources 128 such as the World Wide Web, or withexternal application-specific content such as navigation services,music, video, search services, and the like. Each of the applications112 may contain their own copy of the ASR client 118, or may share oneor more ASR clients 118 using standard software practices on the mobilecommunications facility 118. Each of the applications 112 may maintainstate and present their own interfaces to the user or may shareinformation across applications. Applications may include music orcontent players, search applications for general, local, on-device, orcontent search, voice dialing applications, calendar applications,navigation applications, email, SMS, instant messaging or othermessaging applications, social networking applications, location-basedapplications, games, and the like. In embodiments speech recognitionmodels may be conditioned based on usage of the applications. In certainpreferred embodiments, a speech recognition model may be selected basedon which of the multiple applications running on a mobile device is usedin connection with the ASR client 118 for the speech that is captured ina particular instance of use.

FIG. 2 depicts the architecture for the ASR server infrastructure 102,containing functional blocks for the ASR client 118, ASR router 202, ASRserver 204, ASR engine 208, recognition models 218, usage data 212,human transcription 210, adaptation process 214, external informationsources 124, and user 130 database 104. In a typical deploymentscenario, multiple ASR servers 204 may be connected to an ASR router202; many ASR clients 118 may be connected to multiple ASR routers 102and network traffic load balancers may be presented between ASR clients118 and ASR routers 202. The ASR client 118 may present a graphical user130 interface to the user 130, and establishes a connection with the ASRrouter 202. The ASR client 118 may pass information to the ASR router202, including a unique identifier for the individual phone (client ID)that may be related to a user 130 account created during a subscriptionprocess, and the type of phone (phone ID). The ASR client 118 maycollect audio from the user 130. Audio may be compressed into a smallerformat. Compression may include standard compression scheme used forhuman-human conversation, or a specific compression scheme optimized forspeech recognition. The user 130 may indicate that the user 130 wouldlike to perform recognition. Indication may be made by way of pressingand holding a button for the duration the user 130 is speaking.Indication may be made by way of pressing a button to indicate thatspeaking will begin, and the ASR client 118 may collect audio until itdetermines that the user 130 is done speaking, by determining that therehas been no speech within some pre-specified time period. Inembodiments, voice activity detection may be entirely automated withoutthe need for an initial key press, such as by voice trained command, byvoice command specified on the display of the mobile communicationsfacility 120, or the like.

The ASR client 118 may pass audio, or compressed audio, to the ASRrouter 202. The audio may be sent after all audio is collected orstreamed while the audio is still being collected. The audio may includeadditional information about the state of the ASR client 118 andapplication 112 in which this client is embedded. This additionalinformation, plus the client ID and phone ID, comprises at least aportion of the client state information. This additional information mayinclude an identifier for the application; an identifier for theparticular text field of the application; an identifier for contentbeing viewed in the current application, the URL of the current web pagebeing viewed in a browser for example; or words which are alreadyentered into a current text field. There may be information about whatwords are before and after the current cursor location, oralternatively, a list of words along with information about the currentcursor location. This additional information may also include otherinformation available in the application 112 or mobile communicationfacility 120 which may be helpful in predicting what users 130 may speakinto the application 112 such as the current location of the phone,information about content such as music or videos stored on the phone,history of usage of the application, time of day, and the like.

The ASR client 118 may wait for results to come back from the ASR router202. Results may be returned as word strings representing the system'shypothesis about the words, which were spoken. The result may includealternate choices of what may have been spoken, such as choices for eachword, choices for strings of multiple words, or the like. The ASR client118 may present words to the user 130, that appear at the current cursorposition in the text box, or shown to the user 130 as alternate choicesby navigating with the keys on the mobile communications facility 120.The ASR client 118 may allow the user 130 to correct text by using acombination of selecting alternate recognition hypotheses, navigating towords, seeing list of alternatives, navigating to desired choice,selecting desired choice, deleting individual characters, using somedelete key on the keypad or touch screen; deleting entire words one at atime; inserting new characters by typing on the keypad; inserting newwords by speaking; replacing highlighted words by speaking; or the like.The list of alternatives may be alternate words or strings of word, ormay make use of application constraints to provide a list of alternateapplication-oriented items such as songs, videos, search topics or thelike. The ASR client 118 may also give a user 130 a means to indicatethat the user 130 would like the application to take some action basedon the input text; sending the current state of the input text (acceptedtext) back to the ASR router 202 when the user 130 selects theapplication action based on the input text; logging various informationabout user 130 activity by keeping track of user 130 actions, such astiming and content of keypad or touch screen actions, or corrections,and periodically sending it to the ASR router 202; or the like.

The ASR router 202 may provide a connection between the ASR client 118and the ASR server 204. The ASR router 202 may wait for connectionrequests from ASR clients 118. Once a connection request is made, theASR router 202 may decide which ASR server 204 to use for the sessionfrom the ASR client 118. This decision may be based on the current loadon each ASR server 204; the best predicted load on each ASR server 204;client state information; information about the state of each ASR server204, which may include current recognition models 218 loaded on the ASRengine 208 or status of other connections to each ASR server 204;information about the best mapping of client state information to serverstate information; routing data which comes from the ASR client 118 tothe ASR server 204; or the like. The ASR router 202 may also route data,which may come from the ASR server 204, back to the ASR client 118.

The ASR server 204 may wait for connection requests from the ASR router202. Once a connection request is made, the ASR server 204 may decidewhich recognition models 218 to use given the client state informationcoming from the ASR router 202. The ASR server 204 may perform any tasksneeded to get the ASR engine 208 ready for recognition requests from theASR router 202. This may include pre-loading recognition models 218 intomemory or doing specific processing needed to get the ASR engine 208 orrecognition models 218 ready to perform recognition given the clientstate information. When a recognition request comes from the ASR router202, the ASR server 204 may perform recognition on the incoming audioand return the results to the ASR router 202. This may includedecompressing the compressed audio information, sending audio to the ASRengine 208, getting results back from the ASR engine 208, optionallyapplying a process to alter the words based on the text and on theClient State Information (changing “five dollars” to $5 for example),sending resulting recognized text to the ASR router 202, and the like.The process to alter the words based on the text and on the Client StateInformation may depend on the application 112, for example applyingaddress-specific changes (changing “seventeen dunster street” to “17dunster st.”) in a location-based application 112 such as navigation orlocal search, applying internet-specific changes (changing “yahoo dotcom” to “yahoo.com”) in a search application 112, and the like.

The ASR router 202 may be a standard internet protocol or http protocolrouter, and the decisions about which ASR server to use may beinfluenced by standard rules for determining best servers based on loadbalancing rules and on content of headers or other information in thedata or metadata passed between the ASR client 118 and ASR server 204.

In the case where the speech recognition facility is built-into adevice, each of these components may be simplified or non-existent.

The ASR server 204 may log information to the usage data 212 storage.This logged information may include audio coming from the ASR router202, client state information, recognized text, accepted text, timinginformation, user 130 actions, and the like. The ASR server 204 may alsoinclude a mechanism to examine the audio data and decide if the currentrecognition models 218 are not appropriate given the characteristics ofthe audio data and the client state information. In this case the ASRserver 204 may load new or additional recognition models 218, dospecific processing needed to get ASR engine 208 or recognition models218 ready to perform recognition given the client state information andcharacteristics of the audio data, rerun the recognition based on thesenew models, send back information to the ASR router 202 based on theacoustic characteristics causing the ASR to send the audio to adifferent ASR server 204, and the like.

The ASR engine 208 may utilize a set of recognition models 218 toprocess the input audio stream, where there may be a number ofparameters controlling the behavior of the ASR engine 208. These mayinclude parameters controlling internal processing components of the ASRengine 208, parameters controlling the amount of processing that theprocessing components will use, parameters controlling normalizations ofthe input audio stream, parameters controlling normalizations of therecognition models 218, and the like. The ASR engine 208 may outputwords representing a hypothesis of what the user 130 said and additionaldata representing alternate choices for what the user 130 may have said.This may include alternate choices for the entire section of audio;alternate choices for subsections of this audio, where subsections maybe phrases (strings of one or more words) or words; scores related tothe likelihood that the choice matches words spoken by the user 130; orthe like. Additional information supplied by the ASR engine 208 mayrelate to the performance of the ASR engine 208. The core speechrecognition engine 208 may include automated speech recognition (ASR),and may utilize a plurality of models 218, such as acoustic models 220,pronunciations 222, vocabularies 224, language models 228, and the like,in the analysis and translation of user 130 inputs. Personal languagemodels 228 may be biased for first, last name in an address book, user's130 location, phone number, past usage data, or the like. As a result ofthis dynamic development of user 130 speech profiles, the user 130 maybe free from constraints on how to speak; there may be no grammaticalconstraints placed on the mobile user 130, such as having to saysomething in a fixed domain. The user 130 may be able to say anythinginto the user's 130 mobile communications facility 120, allowing theuser 130 to utilize text messaging, searching, entering an address, orthe like, and ‘speaking into’ the text field, rather than having to typeeverything.

The recognition models 218 may control the behavior of the ASR engine208. These models may contain acoustic models 220, which may control howthe ASR engine 208 maps the subsections of the audio signal to thelikelihood that the audio signal corresponds to each possible soundmaking up words in the target language. These acoustic models 220 may bestatistical models, Hidden Markov models, may be trained on transcribedspeech coming from previous use of the system (training data), multipleacoustic models with each trained on portions of the training data,models specific to specific users 130 or groups of users 130, or thelike. These acoustic models may also have parameters controlling thedetailed behavior of the models. The recognition models 218 may includeacoustic mappings, which represent possible acoustic transformationeffects, may include multiple acoustic mappings representing differentpossible acoustic transformations, and these mappings may apply to thefeature space of the ASR engine 208. The recognition models 218 mayinclude representations of the pronunciations 222 of words in the targetlanguage. These pronunciations 222 may be manually created by humans,derived through a mechanism which converts spelling of words to likelypronunciations, derived based on spoken samples of the word, and mayinclude multiple possible pronunciations for each word in the vocabulary224, multiple sets of pronunciations for the collection of words in thevocabulary 224, and the like. The recognition models 218 may includelanguage models 228, which represent the likelihood of various wordsequences that may be spoken by the user 130. These language models 228may be statistical language models, n-gram statistical language models,conditional statistical language models which take into account theclient state information, may be created by combining the effects ofmultiple individual language models, and the like. The recognitionmodels 218 may include multiple language models 228 which may be used ina variety of combinations by the ASR engine 208. The multiple languagemodels 228 may include language models 228 meant to represent the likelyutterances of a particular user 130 or group of users 130. The languagemodels 228 may be specific to the application 112 or type of application112.

In embodiments, methods and systems disclosed herein may functionindependent of the structured grammar required in most conventionalspeech recognition systems. As used herein, references to “unstructuredgrammar” and “unstructured language models” should be understood toencompass language models and speech recognition systems that allowspeech recognition systems to recognize a wide variety of input fromusers by avoiding rigid constraints or rules on what words can followother words. One implementation of an unstructured language model is touse statistical language models, as described throughout thisdisclosure, which allow a speech recognition system to recognize anypossible sequence of a known list of vocabulary items with the abilityto assign a probability to any possible word sequence. Oneimplementation of statistical language models is to use n-gram models,which model probabilities of sequences of n words. These n-gramprobabilities are estimated based on observations of the word sequencesin a set of training or adaptation data. Such a statistical languagemodel typically has estimation strategies for approximating theprobabilities of unseen n-gram word sequences, typically based onprobabilities of shorter sequences of words (so, a 3-gram model wouldmake use of 2-gram and 1-gram models to estimate probabilities of 3-gramword sequences which were not well represented in the training data).References throughout to unstructured grammars, unstructured languagemodels, and operation independent of a structured grammar or languagemodel encompass all such language models, including such statisticallanguage models.

The multiple language models 228 may include language models 228designed to model words, phrases, and sentences used by people speakingdestinations for a navigation or local search application 112 or thelike. These multiple language models 228 may include language models 228about locations, language models 228 about business names, languagemodels 228 about business categories, language models 228 about pointsof interest, language models 228 about addresses, and the like. Each ofthese types of language models 228 may be general models which providebroad coverage for each of the particular type of ways of entering adestination or may be specific models which are meant to model theparticular businesses, business categories, points of interest, oraddresses which appear only within a particular geographic region.

The multiple language models 228 may include language models 228designed to model words, phrases, and sentences used by people speakinginto messaging applications 112. These language models 228 may includelanguage models 228 specific to addresses, headers, and content fieldsof a messaging application 112. These multiple language models 228 maybe specific to particular types of messages or messaging application 112types.

The multiple language models 228 may include language models 228designed to model words, phrases, and sentences used by people speakingsearch terms for content such as music, videos, games, and the like.These multiple language models 228 may include language models 228representing artist names, song names, movie titles, TV show, popularartists, and the like. These multiple language models 228 may bespecific to various types of content such as music or video category ormay cover multiple categories.

The multiple language models 228 may include language models 228designed to model words, phrases, and sentences used by people speakinggeneral search terms into a search application. The multiple languagemodels 228 may include language models 228 for particular types ofsearch including content search, local search, business search, peoplesearch, and the like.

The multiple language models 228 may include language models 228designed to model words, phrases, and sentences used by people speakingtext into a general internet browser. These multiple language models 228may include language models 228 for particular types of web pages ortext entry fields such as search, form filling, dates, times, and thelike.

Usage data 212 may be a stored set of usage data 212 from the users 130of the service that includes stored digitized audio that may becompressed audio; client state information from each audio segment;accepted text from the ASR client 118; logs of user 130 behavior, suchas key-presses; and the like. Usage data 212 may also be the result ofhuman transcription 210 of stored audio, such as words that were spokenby user 130, additional information such as noise markers, andinformation about the speaker such as gender or degree of accent, or thelike.

Human transcription 210 may be software and processes for a human tolisten to audio stored in usage data 212, and annotate data with wordswhich were spoken, additional information such as noise markers,truncated words, information about the speaker such as gender or degreeof accent, or the like. A transcriber may be presented with hypothesizedtext from the system or presented with accepted text from the system.The human transcription 210 may also include a mechanism to targettranscriptions to a particular subset of usage data 212. This mechanismmay be based on confidence scores of the hypothesized transcriptionsfrom the ASR server 204.

The adaptation process 214 may adapt recognition models 218 based onusage data 212. Another criterion for adaptation 214 may be to reducethe number of errors that the ASR engine 208 would have made on theusage data 212, such as by rerunning the audio through the ASR engine208 to see if there is a better match of the recognized words to whatthe user 130 actually said. The adaptation 214 techniques may attempt toestimate what the user 130 actually said from the annotations of thehuman transcription 210, from the accepted text, from other informationderived from the usage data 212, or the like. The adaptation 214techniques may also make use of client state information 514 to producerecognition models 218 that are personalized to an individual user 130or group of users 130. For a given user 130 or group of users 130, thesepersonalized recognition models 218 may be created from usage data 212for that user 130 or group, as well as data from users 130 outside ofthe group such as through collaborative-filtering techniques todetermine usage patterns from a large group of users 130. The adaptationprocess 214 may also make use of application information to adaptrecognition models 218 for specific domain applications 112 or textfields within domain applications 112. The adaptation process 214 maymake use of information in the usage data 212 to adapt multiple languagemodels 228 based on information in the annotations of the humantranscription 210, from the accepted text, from other informationderived from the usage data 212, or the like. The adaptation process 214may make use of external information sources 124 to adapt therecognition models 218. These external information sources 124 maycontain recordings of speech, may contain information about thepronunciations of words, may contain examples of words that users 130may speak into particular applications, may contain examples of phrasesand sentences which users 130 may speak into particular applications,and may contain structured information about underlying entities orconcepts that users 130 may speak about. The external informationsources 124 may include databases of location entities including cityand state names, geographic area names, zip codes, business names,business categories, points of interest, street names, street numberranges on streets, and other information related to locations anddestinations. These databases of location entities may include linksbetween the various entities such as which businesses and streets appearin which geographic locations and the like. The external information 124may include sources of popular entertainment content such as music,videos, games, and the like. The external information 124 may includeinformation about popular search terms, recent news headlines, or othersources of information which may help predict what users may speak intoa particular application 112. The external information sources 124 maybe specific to a particular application 112, group of applications 112,user 130, or group of users 130. The external information sources 124may include pronunciations of words that users may use. The externalinformation 124 may include recordings of people speaking a variety ofpossible words, phrases, or sentences. The adaptation process 214 mayinclude the ability to convert structured information about underlyingentities or concepts into words, phrases, or sentences which users 130may speak in order to refer to those entities or concepts. Theadaptation process 214 may include the ability to adapt each of themultiple language models 228 based on relevant subsets of the externalinformation sources 124 and usage data 212. This adaptation 214 oflanguage models 228 on subsets of external information source 124 andusage data 212 may include adapting geographic location-specificlanguage models 228 based on location entities and usage data 212 fromonly that geographic location, adapting application-specific languagemodels based on the particular application 112 type, adaptation 124based on related data or usages, or may include adapting 124 languagemodels 228 specific to particular users 130 or groups of users 130 onusage data 212 from just that user 130 or group of users 130.

The user database 104 may be updated by a web registration 108 process,by new information coming from the ASR router 202, by new informationcoming from the ASR server 204, by tracking application usagestatistics, or the like. Within the user database 104 there may be twoseparate databases, the ASR database and the user database 104. The ASRdatabase may contain a plurality of tables, such as asr_servers;asr_routers; asr_am (AM, profile name & min server count); asr_monitor(debugging), and the like. The user 130 database 104 may also contain aplurality of tables, such as a clients table including client ID, user130 ID, primary user 130 ID, phone number, carrier, phone make, phonemodel, and the like; a users 130 table including user 130 ID, developerpermissions, registration time, last activity time, activity countrecent AM ID, recent LM ID, session count, last session timestamp, AM ID(default AM for user 130 used from priming), and the like; a user 130preferences table including user 130 ID, sort, results, radius, savedsearches, recent searches, home address, city, state (for geocoding),last address, city, state (for geocoding), recent locations, city tostate map (used to automatically disambiguate one-to-many city/staterelationship) and the like; user 130 private table including user 130ID, first and last name, email, password, gender, type of user 130 (e.g.data collection, developer, VIP, etc), age and the like; user 130parameters table including user 130 ID, recognition server URL, proxyserver URL, start page URL, logging server URL, logging level,isLogging, isDeveloper, or the like; clients updates table used to sendupdate notices to clients, including client ID, last known version,available version, minimum available version, time last updated, timelast reminded, count since update available, count since last reminded,reminders sent, reminder count threshold, reminder time threshold,update URL, update version, update message, and the like; or othersimilar tables, such as application usage data 212 not related to ASR.

FIG. 2A depicts the case where a tagger 230 is used by the ASR server204 to tag the recognized words according to a set of types of queries,words, or information. For example, in a navigation system 150, thetagging may be used to indicate whether a given utterance by a user is adestination entry or a business search. In addition, the tagging may beused to indicate which words in the utterance are indicative of each ofa number of different information types in the utterance such as streetnumber, street name, city name, state name, zip code, and the like. Forexample in a navigation application, if the user said “navigate to 17dunster street Cambridge Mass.”, the tagging may be [type=navigate][state=MA] [city=Cambridge] [street=dunster] [street_number=17]. The setof tags and the mapping between word strings and tag sets may depend onthe application. The tagger 230 may get words and other information fromthe ASR server 204, or alternatively directly from the ASR engine 208,and may make use of recognition models 218, including tagger models 232specifically designed for this task. In one embodiment, the taggermodels 232 may include statistical models indicating the likely type andmeaning of words (for example “Cambridge” has the highest probability ofbeing a city name, but can also be a street name or part of a businessname), may include a set of transition or parse probabilities (forexample, street names tend to come before city names in a navigationquery), and may include a set of rules and algorithms to determine thebest set of tags for a given input. The tagger 230 may produce a singleset of tags for a given word string, or may produce multiple possibletags sets for the given word string and provide these to theapplication. Each of the tag results may include probabilities or otherscores indicating the likelihood or certainty of the tagging of theinput word string.

FIG. 2B depicts the case where real time human transcription 240 is usedto augment the ASR engine 208. The real time human transcription 240 maybe used to verify or correct the output of the ASR engine before it istransmitted to the ASR client 118. The may be done on all or a subset ofthe user 130 input. If on a subset, this subset may be based onconfidence scores or other measures of certainty from the ASR engine 208or may be based on tasks where it is already known that the ASR engine208 may not perform well enough. The output of the real time humantranscription 240 may be fed back into the usage data 212. Theembodiments of FIGS. 2, 2A and 2B may be combined in various ways sothat, for example, real-time human transcription and tagging mayinteract with the ASR server and other aspects of the ASR serverinfrastructure.

FIG. 3 depicts an example browser-based application infrastructurearchitecture 300 including the browser rendering facility 302, thebrowser proxy 604, text-to-speech (TTS) server 308, TTS engine 310,speech aware mobile portal (SAMP) 312, text-box router 314, domainapplications 312, scrapper 320, user 130 database 104, and the WorldWide Web 330. The browser rendering facility 302 may be a part of theapplication code 114 in the user's mobile communication facility 120 andmay provide a graphical and speech user interface for the user 130 anddisplay elements on screen-based information coming from browser proxy304. Elements may include text elements, image elements, link elements,input elements, format elements, and the like. The browser renderingfacility 302 may receive input from the user 130 and send it to thebrowser proxy 304. Inputs may include text in a text-box, clicks on alink, clicks on an input element, or the like. The browser renderingfacility 302 also may maintain the stack required for “Back” keypresses, pages associated with each tab, and cache recently-viewed pagesso that no reads from proxy are required to display recent pages (suchas “Back”).

The browser proxy 304 may act as an enhanced HTML browser that issueshttp requests for pages, http requests for links, interprets HTML pages,or the like. The browser proxy 304 may convert user 130 interfaceelements into a form required for the browser rendering facility 302.The browser proxy 304 may also handle TTS requests from the browserrendering facility 302; such as sending text to the TTS server 308;receiving audio from the TTS server 308 that may be in compressedformat; sending audio to the browser rendering facility 302 that mayalso be in compressed format; and the like.

Other blocks of the browser-based application infrastructure 300 mayinclude a TTS server 308, TTS engine 310, SAMP 312, user 130 database104 (previously described), the World Wide Web 330, and the like. TheTTS server 308 may accept TTS requests, send requests to the TTS engine310, receive audio from the TTS engine 310, send audio to the browserproxy 304, and the like. The TTS engine 310 may accept TTS requests,generate audio corresponding to words in the text of the request, sendaudio to the TTS server 308, and the like. The SAMP 312 may handleapplication requests from the browser proxy 304, behave similar to a webapplication 330, include a text-box router 314, include domainapplications 318, include a scrapper 320, and the like. The text-boxrouter 314 may accept text as input, similar to a search engine's searchbox, semantically parsing input text using geocoding, key word andphrase detection, pattern matching, and the like. The text-box router314 may also route parse requests accordingly to appropriate domainapplications 318 or the World Wide Web 330. Domain applications 318 mayrefer to a number of different domain applications 318 that may interactwith content on the World Wide Web 330 to provide application-specificfunctionality to the browser proxy. And finally, the scrapper 320 mayact as a generic interface to obtain information from the World Wide Web330 (e.g., web services, SOAP, RSS, HTML, scrapping, and the like) andformatting it for the small mobile screen.

FIG. 4 depicts some of the components of the ASR Client 118. The ASRclient 118 may include an audio capture 402 component which may wait forsignals to begin and end recording, interacts with the built-in audiofunctionality on the mobile communication facility 120, interact withthe audio compression 408 component to compress the audio signal into asmaller format, and the like. The audio capture 402 component mayestablish a data connection over the data network using the servercommunications component 410 to the ASR server infrastructure 102 usinga protocol such as TCP or HTTP. The server communications 410 componentmay then wait for responses from the ASR server infrastructure 102indicated words which the user may have spoken. The correction interface404 may display words, phrases, sentences, or the like, to the user, 130indicating what the user 130 may have spoken and may allow the user 130to correct or change the words using a combination of selectingalternate recognition hypotheses, navigating to words, seeing list ofalternatives, navigating to desired choice, selecting desired choice;deleting individual characters, using some delete key on the keypad ortouch screen; deleting entire words one at a time; inserting newcharacters by typing on the keypad; inserting new words by speaking;replacing highlighted words by speaking; or the like. Audio compression408 may compress the audio into a smaller format using audio compressiontechnology built into the mobile communication facility 120, or by usingits own algorithms for audio compression. These audio compression 408algorithms may compress the audio into a format which can be turned backinto a speech waveform, or may compress the audio into a format whichcan be provided to the ASR engine 208 directly or uncompressed into aformat which may be provided to the ASR engine 208. Servercommunications 410 may use existing data communication functionalitybuilt into the mobile communication facility 120 and may use existingprotocols such as TCP, HTTP, and the like.

FIG. 5A depicts the process 500A by which multiple language models maybe used by the ASR engine. For the recognition of a given utterance, afirst process 504 may decide on an initial set of language models 228for the recognition. This decision may be made based on the set ofinformation in the client state information 514, including applicationID, user ID, text field ID, current state of application 112, orinformation such as the current location of the mobile communicationfacility 120. The ASR engine 208 may then run 508 using this initial setof language models 228 and a set of recognition hypotheses created basedon this set of language models 228. There may then be a decision process510 to decide if additional recognition passes 508 are needed withadditional language models 228. This decision 510 may be based on theclient state information 514, the words in the current set ofrecognition hypotheses, confidence scores from the most recentrecognition pass, and the like. If needed, a new set of language models228 may be determined 518 based on the client state information 514 andthe contents of the most recent recognition hypotheses and another passof recognition 508 made by the ASR engine 208. Once complete, therecognition results may be combined to form a single set of words andalternates to pass back to the ASR client 118.

FIG. 5B depicts the process 500B by which multiple language models 228may be used by the ASR engine 208 for an application 112 that allowsspeech input 502 about locations, such as a navigation, local search, ordirectory assistance application 112. For the recognition of a givenutterance, a first process 522 may decide on an initial set of languagemodels 228 for the recognition. This decision may be made based on theset of information in the client state information 524, includingapplication ID, user ID, text field ID, current state of application112, or information such as the current location of the mobilecommunication facility 120. This client state information may alsoinclude favorites or an address book from the user 130 and may alsoinclude usage history for the application 112. The decision about theinitial set of language models 228 may be based on likely target citiesfor the query 522. The initial set of language models 228 may includegeneral language models 228 about business names, business categories,city and state names, points of interest, street addresses, and otherlocation entities or combinations of these types of location entities.The initial set of language models 228 may also include models 228 foreach of the types of location entities specific to one or moregeographic regions, where the geographic regions may be based on thephone's current geographic location, usage history for the particularuser 130, or other information in the navigation application 112 whichmay be useful in predicting the likely geographic area the user 130 maywant to enter into the application 112. The initial set of languagemodels 228 may also include language models 228 specific to the user 130or group to which the user 130 belongs. The ASR engine 208 may then run508 using this initial set of language models 228 and a set ofrecognition hypotheses created based on this set of language models 228.There may then be a decision process 510 to decide if additionalrecognition passes 508 are needed with additional language models 228.This decision 510 may be based on the client state information 524, thewords in the current set of recognition hypotheses, confidence scoresfrom the most recent recognition pass, and the like. This decision mayinclude determining the likely geographic area of the utterance andcomparing that to the assumed geographic area or set of areas in theinitial language models 228. This determining the likely geographic areaof the utterance may include looking for words in the hypothesis or setof hypotheses, which may correspond to a geographic region. These wordsmay include names for cities, states, areas and the like or may includea string of words corresponding to a spoken zip code. If needed, a newset of language models 228 may be determined 528 based on the clientstate information 524 and the contents of the most recent recognitionhypotheses and another pass of recognition 508 made by the ASR engine208. This new set of language models 228 may include language models 228specific to a geographic region determined from a hypothesis or set ofhypotheses from the previous recognition pass Once complete, therecognition results may be combined 512 to form a single set of wordsand alternates to pass back 520 to the ASR client 118.

FIG. 5C depicts the process 500C by which multiple language models 228may be used by the ASR engine 208 for a messaging application 112 suchas SMS, email, instant messaging, and the like, for speech input 502.For the recognition of a given utterance, a first process 532 may decideon an initial set of language models 228 for the recognition. Thisdecision may be made based on the set of information in the client stateinformation 534, including application ID, user ID, text field ID, orcurrent state of application 112. This client state information mayinclude an address book or contact list for the user, contents of theuser's messaging inbox and outbox, current state of any text entered sofar, and may also include usage history for the application 112. Thedecision about the initial set of language models 228 may be based onthe user 130, the application 112, the type of message, and the like.The initial set of language models 228 may include general languagemodels 228 for messaging applications 112, language models 228 forcontact lists and the like. The initial set of language models 228 mayalso include language models 228 that are specific to the user 130 orgroup to which the user 130 belongs. The ASR engine 208 may then run 508using this initial set of language models 228 and a set of recognitionhypotheses created based on this set of language models 228. There maythen be a decision process 510 to decide if additional recognitionpasses 508 are needed with additional language models 228. This decision510 may be based on the client state information 534, the words in thecurrent set of recognition hypotheses, confidence scores from the mostrecent recognition pass, and the like. This decision may includedetermining the type of message entered and comparing that to theassumed type of message or types of messages in the initial languagemodels 228. If needed, a new set of language models 228 may bedetermined 538 based on the client state information 534 and thecontents of the most recent recognition hypotheses and another pass ofrecognition 508 made by the ASR engine 208. This new set of languagemodels 228 may include language models specific to the type of messagesdetermined from a hypothesis or set of hypotheses from the previousrecognition pass Once complete, the recognition results may be combined512 to form a single set of words and alternates to pass back 520 to theASR client 118.

FIG. 5D depicts the process 500D by which multiple language models 228may be used by the ASR engine 208 for a content search application 112such as music download, music player, video download, video player, gamesearch and download, and the like, for speech input 502. For therecognition of a given utterance, a first process 542 may decide on aninitial set of language models 228 for the recognition. This decisionmay be made based on the set of information in the client stateinformation 544, including application ID, user ID, text field ID, orcurrent state of application 112. This client state information mayinclude information about the user's content and play lists, either onthe client itself or stored in some network-based storage, and may alsoinclude usage history for the application 112. The decision about theinitial set of language models 228 may be based on the user 130, theapplication 112, the type of content, and the like. The initial set oflanguage models 228 may include general language models 228 for search,language models 228 for artists, composers, or performers, languagemodels 228 for specific content such as song and album names, movie andTV show names, and the like. The initial set of language models 228 mayalso include language models 228 specific to the user 130 or group towhich the user 130 belongs. The ASR engine 208 may then run 508 usingthis initial set of language models 228 and a set of recognitionhypotheses created based on this set of language models 228. There maythen be a decision process 510 to decide if additional recognitionpasses 508 are needed with additional language models 228. This decision510 may be based on the client state information 544, the words in thecurrent set of recognition hypotheses, confidence scores from the mostrecent recognition pass, and the like. This decision may includedetermining the type of content search and comparing that to the assumedtype of content search in the initial language models 228. If needed, anew set of language models 228 may be determined 548 based on the clientstate information 544 and the contents of the most recent recognitionhypotheses and another pass of recognition 508 made by the ASR engine208. This new set of language models 228 may include language models 228specific to the type of content search determined from a hypothesis orset of hypotheses from the previous recognition pass Once complete, therecognition results may be combined 512 to form a single set of wordsand alternates to pass back 520 to the ASR client 118.

FIG. 5E depicts the process 500E by which multiple language models 228may be used by the ASR engine 208 for a search application 112 such asgeneral web search, local search, business search, and the like, forspeech input 502. For the recognition of a given utterance, a firstprocess 552 may decide on an initial set of language models 228 for therecognition. This decision may be made based on the set of informationin the client state information 554, including application ID, user ID,text field ID, or current state of application 112. This client stateinformation may include information about the phone's location, and mayalso include usage history for the application 112. The decision aboutthe initial set of language models 228 may be based on the user 130, theapplication 112, the type of search, and the like. The initial set oflanguage models 228 may include general language models 228 for search,language models 228 for different types of search such as local search,business search, people search, and the like. The initial set oflanguage models 228 may also include language models 228 specific to theuser or group to which the user belongs. The ASR engine 208 may then run508 using this initial set of language models 228 and a set ofrecognition hypotheses created based on this set of language models 228.There may then be a decision process 510 to decide if additionalrecognition passes 508 are needed with additional language models 228.This decision 510 may be based on the client state information 554, thewords in the current set of recognition hypotheses, confidence scoresfrom the most recent recognition pass, and the like. This decision mayinclude determining the type of search and comparing that to the assumedtype of search in the initial language models. If needed, a new set oflanguage models 228 may be determined 558 based on the client stateinformation 554 and the contents of the most recent recognitionhypotheses and another pass of recognition 508 made by the ASR engine208. This new set of language models 228 may include language models 228specific to the type of search determined from a hypothesis or set ofhypotheses from the previous recognition pass. Once complete, therecognition results may be combined 512 to form a single set of wordsand alternates to pass back 520 to the ASR client 118.

FIG. 5F depicts the process 500F by which multiple language models 228may be used by the ASR engine 208 for a general browser as amobile-specific browser or general internet browser for speech input502. For the recognition of a given utterance, a first process 562 maydecide on an initial set of language models 228 for the recognition.This decision may be made based on the set of information in the clientstate information 564, including application ID, user ID, text field ID,or current state of application 112. This client state information mayinclude information about the phone's location, the current web page,the current text field within the web page, and may also include usagehistory for the application 112. The decision about the initial set oflanguage models 228 may be based on the user 130, the application 112,the type web page, type of text field, and the like. The initial set oflanguage models 228 may include general language models 228 for search,language models 228 for date and time entry, language models 228 fordigit string entry, and the like. The initial set of language models 228may also include language models 228 specific to the user 130 or groupto which the user 130 belongs. The ASR engine 208 may then run 508 usingthis initial set of language models 228 and a set of recognitionhypotheses created based on this set of language models 228. There maythen be a decision process 510 to decide if additional recognitionpasses 508 are needed with additional language models 228. This decision510 may be based on the client state information 564, the words in thecurrent set of recognition hypotheses, confidence scores from the mostrecent recognition pass, and the like. This decision may includedetermining the type of entry and comparing that to the assumed type ofentry in the initial language models 228. If needed, a new set oflanguage models 228 may be determined 568 based on the client stateinformation 564 and the contents of the most recent recognitionhypotheses and another pass of recognition 508 made by the ASR engine208. This new set of language models 228 may include language models 228specific to the type of entry determined from a hypothesis or set ofhypotheses from the previous recognition pass Once complete, therecognition results may be combined 512 to form a single set of wordsand alternates to pass back 520 to the ASR client 118.

The process to combine recognition output may make use of multiplerecognition hypotheses from multiple recognition passes. These multiplehypotheses may be represented as multiple complete sentences or phrases,or may be represented as a directed graph allowing multiple choices foreach word. The recognition hypotheses may include scores representinglikelihood or confidence of words, phrases, or sentences. Therecognition hypotheses may also include timing information about whenwords and phrases start and stop. The process to combine recognitionoutput may choose entire sentences or phrases from the sets ofhypotheses or may construct new sentences or phrases by combining wordsor fragments of sentences or phrases from multiple hypotheses. Thechoice of output may depend on the likelihood or confidence scores andmay take into account the time boundaries of the words and phrases.

FIG. 6 shows the components of the ASR engine 208. The components mayinclude signal processing 602 which may process the input speech eitheras a speech waveform or as parameters from a speech compressionalgorithm and create representations which may be used by subsequentprocessing in the ASR engine 208. Acoustic scoring 604 may use acousticmodels 220 to determine scores for a variety of speech sounds forportions of the speech input. The acoustic models 220 may be statisticalmodels and the scores may be probabilities. The search 608 component maymake use of the score of speech sounds from the acoustic scoring 602 andusing pronunciations 222, vocabulary 224, and language models 228, findthe highest scoring words, phrases, or sentences and may also producealternate choices of words, phrases, or sentences.

FIG. 7 shows an example of how the user 130 interface layout and initialscreen 700 may look on a user's 130 mobile communications facility 120.The layout, from top to bottom, may include a plurality of components,such as a row of navigable tabs, the current page, soft-key labels atthe bottom that can be accessed by pressing the left or right soft-keyson the phone, a scroll-bar on the right that shows vertical positioningof the screen on the current page, and the like. The initial screen maycontain a text-box with a “Search” button, choices of which domainapplications 318 to launch, a pop-up hint for first-time users 130, andthe like. The text box may be a shortcut that users 130 can enter into,or speak into, to jump to a domain application 318, such as “Restaurantsin Cambridge” or “Send a text message to Joe”. When the user 130 selectsthe “Search” button, the text content is sent. Application choices maysend the user 130 to the appropriate application when selected. Thepopup hint 1) tells the user 130 to hold the green TALK button to speak,and 2) gives the user 130 a suggestion of what to say to try the systemout. Both types of hints may go away after several uses.

FIG. 7A depicts using the speech recognition results to providetop-level control or basic functions of a mobile communication device,music device, navigation device, and the like. In this case, the outputsfrom the speech recognition facility may be used to determine andperform an appropriate action of the phone. The process depicted in FIG.7A may start at step 702 to recognize user input, resulting in thewords, numbers, text, phrases, commands, and the like that the userspoke. Optionally at a step 704 user input may be tagged with tags whichhelp determine appropriate actions. The tags may include informationabout the input, such as that the input was a messaging input, an inputindicating the user would like to place a call, an input for a searchengine, and the like. The next step 708 is to determine an appropriateaction, such as by using a combination of words and tags. The system maythen optionally display an action-specific screen at a step 710, whichmay allow a user to alter text and actions at a step 712. Finally, thesystem performs the selected action at a step 714. The actions mayinclude things such as: placing a phone call, answering a phone call,entering text, sending a text message, sending an email message,starting an application 112 resident on the mobile communicationfacility 120, providing an input to an application resident on themobile communication facility 120, changing an option on the mobilecommunication facility 120, setting an option on the mobilecommunication facility 120, adjusting a setting on the mobilecommunication facility 120, interacting with content on the mobilecommunication facility 120, and searching for content on the mobilecommunication facility 120. The perform action step 714 may involveperforming the action directly using built-in functionality on themobile communications facility 120 or may involve starting anapplication 112 resident on the mobile communication facility 120 andhaving the application 112 perform the desired action for the user. Thismay involve passing information to the application 112 which will allowthe application 112 to perform the action such as words spoken by theuser 130 or tagged results indicating aspects of action to be performed.This top level phone control is used to provide the user 130 with anoverall interface to a variety of functionality on the mobilecommunication facility 120. For example, this functionality may beattached to a particular button on the mobile communication facility120. The user 130 may press this button and say something like “call JoeCerra” which would be tagged as [type=call] [name=Joe Cerra], whichwould map to action DIAL, invoking a dialing-specific GUI screen,allowing the user to correct the action or name, or to place the call.Other examples may include the case where the user can say somethinglike “navigate to 17 dunster street Cambridge Mass.”, which would betagged as [type=navigate] [state=MA] [city=Cambridge] [street=dunster][street_number=17], which would be mapped to action NAVIGATE, invoking anavigation-specific GUI screen allowing the user to correct the actionor any of the tags, and then invoking a build-in navigation system onthe mobile communications facility 120. The application which getsinvoked by the top-level phone control may also allow speech entry intoone or more text boxes within the application. So, once the user 130speaks into the top level phone control and an application is invoked,the application may allow further speech input by including the ASRclient 118 in the application. This ASR client 118 may get detailedresults from the top level phone control such that the GUI of theapplication may allow the user 130 to correct the resulting words fromthe speech recognition system including seeing alternate results forword choices.

FIG. 7B shows as an example, a search-specific GUI screen that mayresult if the user says something like “restaurants in Cambridge Mass.”.The determined action 720 is shown in a box which allows the user toclick on the down arrow or other icon to see other action choices (ifthe user wants to send email about “restaurants in Cambridge Mass.” forexample). There is also a text box 722 which shows the words recognizedby the system. This text box 722 may allow the user to alter the text byspeaking, or by using the keypad, or by selecting among alternatechoices from the speech recognizer. The search button 724 allows theuser to carry out the search based on a portion of the text in the textbox 722. Boxes 726 and 728 show alternate choices from the speechrecognizer. The user may click on one of these items to facilitatecarrying out the search based on a portion of the text in one of theseboxes. Selecting box 726 or 728 may cause the text in the selected boxto be exchanged with the text in text box 722.

FIG. 7C shows an embodiment of an SMS-specific GUI screen that mayresult if the user says something like “send SMS to joe cerra let's meetat pete's in harvard square at 7 am”. The determined action 730 is shownin a box which allows the user to click on the down arrow or other iconto see other action choices. There is also a text box 732 which showsthe words recognized as the “to” field. This text box 732 may allow theuser to alter the text by speaking, or by using the keypad, or byselecting among alternate choices from the speech recognizer. Messagetext box 734 shows the words recognized as the message component of theinput. This text box 734 may allow the user to alter the text byspeaking, or by using the keypad, or by selecting among alternatechoices from the speech recognizer. The send button 738 allows the userto send the text message based on the contents of the “to” field and themessage component.

This top-level control may also be applied to other types of devicessuch as music players, navigation systems, or other special orgeneral-purpose devices. In this case, the top-level control allowsusers to invoke functionality or applications across the device usingspeech input.

This top-level control may make use of adaptation to improve the speechrecognition results. This adaptation may make use of history of usage bythe particular user to improve the performance of the recognitionmodels. The adaptation of the recognition models may include adaptingacoustic models, adapting pronunciations, adapting vocabularies, andadapting language models. The adaptation may also make use of history ofusage across many users. The adaptation may make use of any correctionor changes made by the user. The adaptation may also make use of humantranscriptions created after the usage of the system.

This top level control may make use of adaptation to improve theperformance of the word and phrase-level tagging. This adaptation maymake use of history of usage by the particular user to improve theperformance of the models used by the tagging. The adaptation may alsomake use of history of usage by other users to improve the performanceof the models used by the tagging. The adaptation may make use of changeor corrections made by the user. The adaptation may also make use ofhuman transcription of appropriate tags created after the usage of thesystem,

This top level control may make use of adaptation to improve theperformance selection of the action. This adaptation may make use ofhistory of usage by the particular user to improve the performance ofthe models and rules used by this action selection. The adaptation mayalso make use of history of usage by other users to improve theperformance of the models and rules used by the action selection. Theadaptation may make use of change or corrections made by the user. Theadaptation may also make use of human transcription of appropriateactions after the usage of the system. It should be understood thatthese and other forms of adaptation may be used in the variousembodiments disclosed throughout this disclosure where the potential foradaptation is noted.

Although there are mobile phones with full alphanumeric keyboards, mostmass-market devices are restricted to the standard telephone keypad 802,such as shown in FIG. 8. Command keys may include a “TALK”, orgreen-labeled button, which may be used to make a regular voice-basedphone call; an “END” button which is used to terminate a voice-basedcall or end an application and go back to the phone's main screen; afive-way control navigation pad that users may employ to move up, down,left, and right, or select by pressing on the center button (labeled“MENU/OK” in FIG. 8); two soft-key buttons that may be used to selectthe labels at the bottom of the screen; a back button which is used togo back to the previous screen in any application; a delete button usedto delete entered text that on some phones, such as the one pictured inFIG. 8, the delete and back buttons are collapsed into one; and thelike.

FIG. 9 shows text boxes in a navigate-and-edit mode. A text box iseither in navigate mode or edit mode 900. When in navigate mode 902, nocursor or a dim cursor is shown and ‘up/down’, when the text box ishighlighted, moves to the next element on the browser screen. Forexample, moving down would highlight the “search” box. The user 130 mayenter edit mode from navigate mode 902 on any of a plurality of actions;including pressing on center joystick; moving left/right in navigatemode; selecting “Edit” soft-key; pressing any of the keys 0-9, whichalso adds the appropriate letter to the text box at the current cursorposition; and the like. When in edit mode 904, a cursor may be shown andthe left soft-key may be “Clear” rather than “Edit.” The current shiftmode may be also shown in the center of the bottom row. In edit mode904, up and down may navigate within the text box, although users 130may also navigate out of the text box by navigating past the first andlast rows. In this example, pressing up would move the cursor to thefirst row, while pressing down instead would move the cursor out of thetext box and highlight the “search” box instead. The user 130 may holdthe navigate buttons down to perform multiple repeated navigations. Whenthe same key is held down for an extended time, four seconds forexample, navigation may be sped up by moving more quickly, for instance,times four in speed. As an alternative, navigate mode 902 may be removedso that when the text box is highlighted, a cursor may be shown. Thismay remove the modality, but then requires users 130 to move up and downthrough each line of the text box when trying to navigate past the textbox.

Text may be entered in the current cursor position in multi-tap mode, asshown in FIGS. 10, 11, and 12. As an example, pressing “2” once may bethe same as entering “a”, pressing “2” twice may be the same as entering“b”, pressing “2” three times may be the same as entering “c”, andpressing “2” 4 times may be the same as entering “2”. The direction keysmay be used to reposition the cursor. Back, or delete on some phones,may be used to delete individual characters. When Back is held down,text may be deleted to the beginning of the previous recognition result,then to the beginning of the text. Capitalized letters may be entered bypressing the “*” key which may put the text into capitalization mode,with the first letter of each new word capitalized. Pressing “*” againputs the text into all-caps mode, with all new entered letterscapitalized. Pressing “*” yet again goes back to lower case mode whereno new letters may be capitalized. Numbers may be entered either bypressing a key repeatedly to cycle through the letters to the number, orby going into numeric mode. The menu soft-key may contain a “Numbers”option which may put the cursor into numeric mode. Alternatively,numeric mode may be accessible by pressing “*” when cyclingcapitalization modes. To switch back to alphanumeric mode, the user 130may again select the Menu soft-key which now contains an “Alpha” option,or by pressing “*”. Symbols may be entered by cycling through the “1”key, which may map to a subset of symbols, or by bringing up the symboltable through the Menu soft-key. The navigation keys may be used totraverse the symbol table and the center OK button used to select asymbol and insert it at the current cursor position.

FIG. 13 provides examples of speech entry 1300, and how it is depictedon the user 130 interface. When the user 130 holds the TALK button tobegin speaking, a popup may appear informing the user 130 that therecognizer is listening 1302. In addition, the phone may either vibrateor play a short beep to cue the user 130 to begin speaking. When theuser 130 is finished speaking and releases the TALK button, the popupstatus may show “Working” with a spinning indicator. The user 130 maycancel a processing recognition by pressing a button on the keypad ortouch screen, such as “Back” or a directional arrow. Finally, when theresult is received from the ASR server 204, the text box may bepopulated.

Referring to FIG. 14, when the user 130 presses left or right tonavigate through the text box, alternate results 1402 for each word maybe shown in gray below the cursor for a short time, such as 1.7 seconds.After that period, the gray alternates disappear, and the user 130 mayhave to move left or right again to get the box. If the user 130 pressesdown to navigate to the alternates while it is visible, then the currentselection in the alternates may be highlighted, and the words that willbe replaced in the original sentence may be highlighted in red 1404. Theimage on the bottom left of FIG. 14 shows a case where two words in theoriginal sentence will be replaced 1408. To replace the text with thehighlighted alternate, the user 130 may press the center OK key. Whenthe alternate list is shown in red 1408 after the user 130 presses downto choose it, the list may become hidden and go back to normal cursormode if there is no activity after some time, such as 5 seconds. Whenthe alternate list is shown in red, the user 130 may also move out of itby moving up or down past the top or bottom of the list, in which casethe normal cursor is shown with no gray alternates box. When thealternate list is shown in red, the user 130 may navigate the text bywords by moving left and right. For example, when “Nobel” is highlighted1404, moving right would highlight “bookstore” and show its alternatelist instead.

FIG. 15 depicts screens that show navigation and various views ofinformation related to search features of the methods and systems hereindescribed. When the user 130 navigates to a new screen, a “Back” key maybe used to go back to a previous screen. As shown in FIG. 15, if theuser 130 selects “search” on screen 1502 and navigates to screen 1504 or1508, pressing “Back” after looking through the search results ofscreens 1504 or 1508 the screen 1502 may be shown again.

Referring to FIG. 16, when the user 130 navigates to a new page from thehome page, a new tab may be automatically inserted, such as to the rightof the “home” tab, as shown in FIG. 16. Unless the user 130 has selectedto enter or alter entries in a text box, tabs can be navigated bypressing left or right keys on the user interface keypad. The user 130may also move the selection indicator to the top of the screen andselect the tab itself before moving left or right. When the tab ishighlighted, the user 130 may also select a soft-key to remove thecurrent tab and screen. As an alternative, tabs may show icons insteadof names as pictured, tabs may be shown at the bottom of the screen, theinitial screen may be pre-populated with tabs, selection of an item fromthe home page may take the user 130 to an existing tab instead of a newone, and tabs may not be selectable by moving to the top of the screenand tabs may not be removable by the user 130, and the like.

Referring again briefly to FIG. 2, communication may occur among atleast the ASR client 118, ASR router 202, and ASR server 204. Thesecommunications may be subject to specific protocols. In an embodiment ofthese protocols, the ASR client 118, when prompted by user 130, mayrecord audio and may send it to the ASR router 202. Received resultsfrom the ASR router 202 are displayed for the user 130. The user 130 maysend user 130 entries to ASR router 202 for any text entry. The ASRrouter 202 sends audio to the appropriate ASR server 204, based at leaston the user 130 profile represented by the client ID and CPU load on ASRservers 204. The results may then be sent from the ASR server 204 backto the ASR client 118. The ASR router 202 re-routes the data if the ASRserver 204 indicates a mismatched user 130 profile. The ASR router 202sends to the ASR server 204 any user 130 text inputs for editing. TheASR server 204 receives audio from ASR router 202 and performsrecognition. Results are returned to the ASR router 202. The ASR server204 alerts the ASR router 202 if the user's 130 speech no longer matchesthe user's 130 predicted user 130 profile, and the ASR router 202handles the appropriate re-route. The ASR server 204 also receivesuser-edit accepted text results from the ASR router 202.

FIG. 17 shows an illustration of the packet types that are communicatedbetween the ASR client 118, ASR router 202, and server 204 atinitialization and during a recognition cycle. During initialization, aconnection is requested, with the connection request going from ASRclient 118 to the ASR router 202 and finally to the ASR server 204. Aready signal is sent back from the ASR servers 204 to the ASR router 202and finally to the ASR client 118. During the recognition cycle, awaveform is input at the ASR client 118 and routed to the ASR servers204. Results are then sent back out to the ASR client 118, where theuser 130 accepts the returned text, sent back to the ASR servers 104. Aplurality of packet types may be utilized during these exchanges, suchas PACKET_WAVEFORM=1, packet is waveform; PACKET_TEXT=2, packet is text;PACKET_END_OF_STREAM=3, end of waveform stream; PACKET_IMAGE=4, packetis image; PACKET_SYNCLIST=5, syncing lists, such as email lists;PACKET_CLIENT_PARAMETERS=6, packet contains parameter updates forclient; PACKET_ROUTER_CONTROL=7, packet contains router controlinformation; PACKET_MESSAGE=8, packet contains status, warning or errormessage; PACKET_IMAGE_REQUEST=9, packet contains request for an image oricon; or the like.

Referring to FIG. 18, each message may have a header, that may includevarious fields, such as packet version, packet type, length of packet,data flags, unreserved data, and any other dat, fields or content thatis applicable to the message. All multi-byte words may be encoded inbig-endian format.

Referring again to FIG. 17, initialization may be sent from the ASRclient 118, through the ASR router 202, to the ASR server 204. The ASRclient 118 may open a connection with the ASR router 202 by sending itsClient ID. The ASR router 202 in turn looks up the ASR client's 118 mostrecent acoustic model 220 (AM) and language model 228 (LM) and connectsto an appropriate ASR server 204. The ASR router 202 stores thatconnection until the ASR client 118 disconnects or the Model ID changes.The packet format for initialization may have a specific format, such asPacket type=TEXT, Data=ID:<client id string> ClientVersion: <clientversion string>, Protocol:<protocol id string> NumReconnects: <#attempts client has tried reconnecting to socket>, or the like. Thecommunications path for initialization may be (1) Client sends Client IDto ASR router 202, (2) ASR router 202 forwards to ASR a modified packet:Modified Data=<client's original packet data> SessionCount: <sessioncount string> SpeakerID: <user id sting>\0, and (3) resulting state: ASRis now ready to accept utterance(s) from the ASR client 118, ASR router202 maintains client's ASR connection.

As shown in FIG. 17, a ready packet may be sent back to the ASR client118 from the ASR servers 204. The packet format for packet ready mayhave a specific format, such as Packet type=TEXT, Data=Ready\0, and thecommunications path may be (1) ASR sends Ready router and (2) ASR router202 forwards Ready packet to ASR client 118.

As shown in FIG. 17, a field ID packet containing the name of theapplication and text field within the application may be sent from theASR client 118 to the ASR servers 204. This packet is sent as soon asthe user 130 pushes the TALK button to begin dictating one utterance.The ASR servers 204 may use the field ID information to selectappropriate recognition models 142 for the next speech recognitioninvocation. The ASR router 202 may also use the field ID information toroute the current session to a different ASR server 204. The packetformat for the field ID packet may have a specific format, such asPacket type=TEXT; Data=FieldID; <type> <url> <form element name>, forbrowsing mobile web pages; Data=FieldID: message, for SMS text box; orthe like. The connection path may be (1) ASR client 118 sends Field IDto ASR router 202 and (2) ASR router 202 forwards to ASR for logging.

As shown in FIG. 17, a waveform packet may be sent from the ASR client118 to the ASR servers 204. The ASR router 202 sequentially streamsthese waveform packets to the ASR server 204. If the ASR server 204senses a change in the Model ID, it may send the ASR router 202 aROUTER_CONTROL packet containing the new Model ID. In response, the ASRrouter 202 may reroute the waveform by selecting an appropriate ASR andflagging the waveform such that the new ASR server 204 will not performadditional computation to generate another Model ID. The ASR router 202may also re-route the packet if the ASR server's 204 connection drops ortimes out. The ASR router 202 may keep a cache of the most recentutterance, session information such as the client ID and the phone ID,and corresponding FieldID, in case this happens. The packet format forthe waveform packet may have a specific format, such as Packettype=WAVEFORM; Data=audio; with the lower 16 bits of flags set tocurrent Utterance ID of the client. The very first part of WAVEFORMpacket may determine the waveform type, currently only supporting AMR orQCELP, where “#!AMR\n” corresponds to AMR and “RIFF” corresponds toQCELP. The connection path may be (1) ASR client 118 sends initial audiopacket (referred to as the BOS, or beginning of stream) to the ASRrouter 202, (2) ASR router 202 continues streaming packets (regardlessof their type) to the current ASR until one of the following eventsoccur: (a) ASR router 202 receives packet type END_OF_STREAM, signalingthat this is the last packet for the waveform, (b) ASR disconnects ortimes out, in which case ASR router 202 finds new ASR, repeats abovehandshake, sends waveform cache, and continues streaming waveform fromclient to ASR until receives END_OF_STREAM, (c) ASR sends ROUTER_CONTROLto ASR router 202 instructing the ASR router 202 that the Model ID forthat utterance has changed, in which case the ASR router 202 behaves asin ‘b’, (d) ASR client 118 disconnects or times out, in which case thesession is closed, or the like. If the recognizer times out ordisconnects after the waveform is sent then the ASR router 202 mayconnect to a new ASR.

As shown in FIG. 17, a request model switch for utterance packet may besent from the ASR server 204 to the ASR router 202. This packet may besent when the ASR server 204 needs to flag that its user 130 profiledoes not match that of the utterance, i.e. Model ID for the utteranceshas changed. The packet format for the request model switch forutterance packet may have a specific format, such as Packettype=ROUTER_CONTROL; Data=SwitchModelID: AM=<integer> LM=<integer>SessionID=<integer> UttID=<integer>. The communication may be (1) ASRserver 204 sends control packet to ASR router 202 after receiving thefirst waveform packet, and before sending the results packet, and (2)ASR router 202 then finds an ASR which best matches the new Model ID,flags the waveform data such that the new ASR server 204 will not sendanother SwitchModelID packet, and resends the waveform. In addition,several assumptions may be made for this packet, such as the ASR server204 may continue to read the waveform packet on the connection, send aAlternate String or SwitchModelID for every utterance with BOS, and theASR router 202 may receive a switch model id packet, it sets the flagsvalue of the waveform packets to <flag value> & 0x8000 to notify ASRthat this utterance's Model ID does not need to be checked.

As shown in FIG. 17, a done packet may be sent from the ASR server 204to the ASR router 202. This packet may be sent when the ASR server 204has received the last audio packet, such as type END_OF_STREAM. Thepacket format for the done packet may have a specific format, such asPacket type=TEXT; with the lower 16 bits of flags set to Utterance IDand Data=Done\0. The communications path may be (1) ASR sends done toASR router 202 and (2) ASR router 202 forwards to ASR client 118,assuming the ASR client 118 only receives one done packet per utterance.

As shown in FIG. 17, an utterance results packet may be sent from theASR server 204 to the ASR client 118. This packet may be sent when theASR server 204 gets a result from the ASR engine 208. The packet formatfor the utterance results packet may have a specific format, such asPacket type=TEXT, with the lower 16 bits of flags set to Utterance IDand Data=ALTERNATES: <utterance result string>. The communications pathmay be (1) ASR sends results to ASR router 202 and (2) ASR router 202forwards to ASR client 118. The ASR client 118 may ignore the results ifthe Utterance ID does not match that of the current recognition

As shown in FIG. 17, an accepted text packet may be sent from the ASRclient 118 to the ASR server 204. This packet may be sent when the user130 submits the results of a text box, or when the text box loosesfocus, as in the API, so that the recognizer can adapt to correctedinput as well as full-text input. The packet format for the acceptedtext packet may have a specific format, such as Packet type=TEXT, withthe lower 16 bits of flags set to most recent Utterance ID, withData=Accepted Text: <accepted utterance string>. The communications pathmay be (1) ASR client 118 sends the text submitted by the user 130 toASR router 202 and (2) ASR router 202 forwards to ASR server 204 whichrecognized results, where <accepted utterance string> contains the textstring entered into the text box. In embodiments, other logginginformation, such as timing information and user 130 editing keystrokeinformation may also be transferred.

Router control packets may be sent between the ASR client 118, ASRrouter 202, and ASR servers 204, to help control the ASR router 202during runtime. One of a plurality of router control packets may be aget router status packet. The packet format for the get router statuspacket may have a specific format, such as Packet type=ROUTER_CONTROL,with Data=GetRouterStatus\0. The communication path may be one or moreof the following: (1) entity sends this packet to the ASR router 202 and(2) ASR router 202 may respond with a status packet.

FIG. 19 depicts an embodiment of a specific status packet format 1900,that may facilitate determining status of the ASR Router 202, ASR Server204, ASR client 118 and any other element, facility, function, datastate, or information related to the methods and systems hereindisclosed.

Another of a plurality of router control packets may be a busy out ASRserver packet. The packet format for the busy out ASR server packet mayhave a specific format, such as Packet type=ROUTER_CONTROL, withData=BusyOutASRServer: <ASR Server ID>\0. Upon receiving the busy outASR server packet, the ASR router 202 may continue to finish up theexisting sessions between the ASR router 202 and the ASR server 204identified by the <ASR Server ID>, and the ASR router 202 may not starta new session with the said ASR server 204. Once all existing sessionsare finished, the ASR router 202 may remove the said ASR server 204 fromits ActiveServer array. The communication path may be (1) entity sendsthis packet to the ASR router 202 and (2) ASR router 202 responds withACK packet with the following format: Packet type=TEXT, and Data=ACK\0.

Another of a plurality of router control packets may be an immediatelyremove ASR server packet. The packet format for the immediately removeASR server packet may have a specific format, such as Packettype=ROUTER_CONTROL, with Data=RemoveASRServer: <ASR Server ID>\0. Uponreceiving the immediately remove ASR server packet, the ASR router 202may immediately disconnect all current sessions between the ASR router202 and the ASR server 204 identified by the <ASR Server ID>, and theASR router 202 may also immediately remove the said ASR server 204 fromits Active Server array. The communication path may be (1) entity sendsthis packet to the ASR router 202 and (2) ASR router 202 responds withACK packet with the following format: Packet type=TEXT, and Data=ACK\0.

Another of a plurality of router control packets may be an add of an ASRserver 204 to the router packet. When an ASR server 204 is initiallystarted, it may send the router(s) this packet. The ASR router 202 inturn may add this ASR server 204 to its Active Server array afterestablishing this ASR server 204 is indeed functional. The packet formatfor the add an ASR server 204 to the ASR router 202 may have a specificformat, such as Packet type=ROUTER_CONTROL, with Data=AddASRServer:ID=<server id> IP=<server ip address> PORT=<server port> AM=<server AMinteger> LM=<server LM integer> NAME=<server name string>PROTOCOL=<server protocol float>. The communication path may be (1)entity sends this packet to the ASR router 202 and (2) ASR router 202responds with ACK packet with the following format: Packet type=TEXT,and Data=ACK\0.

Another of a plurality of router control packets may be an alter routerlogging format packet. This function may cause the ASR router 202 toread a logging.properties file, and update its logging format duringruntime. This may be useful for debugging purposes. The location of thelogging.properties file may be specified when the ASR router 202 isstarted. The packet format for the alter router logging format may havea specific format, such as Packet type=ROUTER_CONTROL, withData=ReadLogConfigurationFile. The communications path may be (1) entitysends this packet to the ASR router 202 and (2) ASR router 202 respondswith ACK packet with the following format: Packet type=TEXT, andData=ACK\0.

Another of a plurality of router control packets may be a get ASR serverstatus packet. The ASR server 204 may self report the status of thecurrent ASR server 204 with this packet. The packet format for the getASR server 204 status may have a specific format, such as Packettype=ROUTER_CONTROL, with data=RequestStatus\0. The communications pathmay be (1) entity sends this packet to the ASRServer 204 and (2) ASRServer 204 responds with a status packet with the following format:Packet type=TEXT; Data=ASRServerStatus: Status=<1 for ok or 0 for error>AM=<AM id> LM=<LM id> NumSessions=<number of active sessions>NumUtts=<number of queued utterances> TimeSinceLastRec=<seconds sincelast recognizer activity>\n Session: client=<client id> speaker=<speakerid> sessioncount=<sessioncount>\n<other Session: line if other sessionsexist>\n \0. This router control packet may be used by the ASR router202 when establishing whether or not an ASR server 204 is indeedfunctional.

There may be a plurality of message packets associated withcommunications between the ASR client 118, ASR router 202, and ASRservers 204, such as error, warning, and status. The error messagepacket may be associated with an irrecoverable error, the warningmessage packet may be associated with a recoverable error, and a statusmessage packet may be informational. All three types of messages maycontain strings of the format:“<messageType><message>message</message><cause>cause</cause><code>code</code></messageType>”.

Wherein “messageType” is one of either “status,” “warning,” or “error”;“message” is intended to be displayed to the user; “cause” is intendedfor debugging; and “code” is intended to trigger additional actions bythe receiver of the message.

The error packet may be sent when a non-recoverable error occurs and isdetected. After an error packet has been sent, the connection may beterminated in 5 seconds by the originator if not already closed by thereceiver. The packet format for error may have a specific format, suchas Packet type=MESSAGE; and Data=“<error><message>errormessage</message><cause>error cause</cause><code>errorcode</code></error>”. The communication path from ASR client 118 (theoriginator) to ASR server 204 (the receiver) may be (1) ASR client 118sends error packet to ASR server 204, (2) ASR server 204 should closeconnection immediately and handle error, and (3) ASR client 118 willclose connection in 5 seconds if connection is still live. There are anumber of potential causes for the transmission of an error packet, suchas the ASR has received beginning of stream (BOS), but has not receivedend of stream (EOS) or any waveform packets for 20 seconds; a client hasreceived corrupted data; the ASR server 204 has received corrupted data;and the like. Examples of corrupted data may be invalid packet type,checksum mismatch, packet length greater than maximum packet size, andthe like.

The warning packet may be sent when a recoverable error occurs and isdetected. After a warning packet has been sent, the current requestbeing handled may be halted. The packet format for warning may have aspecific format, such as Packet type=MESSAGE;Data=“<warning><message>warning message</message><cause>warningcause</cause><code>warning code</code></warning>”. The communicationspath from ASR client 118 to ASR server 204 may be (1) ASR client 118sends warning packet to ASR server 204 and (2) ASR server 204 shouldimmediately handle the warning. The communications path from ASR server204 to ASR client 118 may be (1) ASR server 204 sends error packet toASR client 118 and (2) ASR client 118 should immediately handle warning.There are a number of potential causes for the transmission of a warningpacket; such as there are no available ASR servers 204 to handle therequest ModelID because the ASR servers 204 are busy.

The status packets may be informational. They may be sent asynchronouslyand do not disturb any processing requests. The packet format for statusmay have a specific format, such as Packet type=MESSAGE;Data=“<status><message>status message</message><cause>statuscause</cause><code>status code</code></status>”. The communications pathfrom ASR client 118 to ASR server 204 may be (1) ASR client 118 sendsstatus packet to ASR server 204 and (2) ASR server 204 should handlestatus. The communication path from ASR server 204 to ASR client 118 maybe (1) ASR server 204 sends status packet to ASR client 118 and (2) ASRclient 118 should handle status. There are a number of potential causesfor the transmission of a status packet, such as an ASR server 204detects a model ID change for a waveform, server timeout, server error,and the like.

The elements depicted in flow charts and block diagrams throughout thefigures imply logical boundaries between the elements. However,according to software or hardware engineering practices, the depictedelements and the functions thereof may be implemented as parts of amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations are within thescope of the present disclosure. Thus, while the foregoing drawings anddescription set forth functional aspects of the disclosed systems, noparticular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context.

Similarly, it will be appreciated that the various steps identified anddescribed above may be varied, and that the order of steps may beadapted to particular applications of the techniques disclosed herein.All such variations and modifications are intended to fall within thescope of this disclosure. As such, the depiction and/or description ofan order for various steps should not be understood to require aparticular order of execution for those steps, unless required by aparticular application, or explicitly stated or otherwise clear from thecontext.

The methods or processes described above, and steps thereof, may berealized in hardware, software, or any combination of these suitable fora particular application. The hardware may include a general-purposecomputer and/or dedicated computing device. The processes may berealized in one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as computer executable codecreated using a structured programming language such as C, an objectoriented programming language such as C++, or any other high-level orlow-level programming language (including assembly languages, hardwaredescription languages, and database programming languages andtechnologies) that may be stored, compiled or interpreted to run on oneof the above devices, as well as heterogeneous combinations ofprocessors, processor architectures, or combinations of differenthardware and software.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, means for performing thesteps associated with the processes described above may include any ofthe hardware and/or software described above. All such permutations andcombinations are intended to fall within the scope of the presentdisclosure.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present invention isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

All documents referenced herein are hereby incorporated by reference.

What is claimed is:
 1. A method of entering text into a music systemusing a processor, comprising: recording speech presented by a userusing a resident capture facility; providing the speech as a recordingto a speech recognition facility; selecting at least one statisticallanguage model, including a large vocabulary statistical language model,from a set of language models based at least in part on contextualinformation relating to the recording, wherein the at least onestatistical language model selected includes a general language modelfor artists, a general language model for song titles, and a generallanguage model for music types; determining that the at least onestatistical language model selected provides insufficient recognitionoutput and requires an additional recognition pass of the recording;conducting the additional recognition pass of the recording; selectingat least one other statistical language model based at least in part onthe additional recognition pass of the recording and client stateinformation of the recording; generating results utilizing the speech ofthe recording recognized by the speech recognition facility; and usingthe results in the music system, wherein the music system providesinformation relating to a music application to the speech recognitionfacility, wherein generating the results is based at least in part onthe information, wherein the information relating to the musicapplication includes contextual information within the musicapplication, and wherein the contextual information includes at leastone of a usage history of the music application and information from atleast one of a favorites list and playlists of the user.
 2. The methodof claim 1, further comprising using user feedback to adapt the at leastone statistical language model selected.
 3. The method of claim 1,wherein the speech recognition facility is remotely located from themusic system.
 4. The method of claim 1, wherein the information relatingto the music application further includes at least one of an identity ofthe music application, an identity of a text box within the musicapplication, an identity of the music system, and an identity of theuser.
 5. The method of claim 4, wherein the contextual informationfurther includes at least one of information about music currentlystored on the music system, and information currently displayed in themusic application.
 6. The method of claim 1, wherein generating theresults based at least in part on the information relating to the musicapplication includes selecting at least one of a plurality ofrecognition models as the at least one statistical language model basedon the information relating to the music application and the recording.7. The method of claim 1, wherein the at least one statistical languagemodel is selected based at least in part on the information relating tomusic application.
 8. The method of claim 1, further comprisingselecting a different statistical language model based on the resultsgenerated.
 9. The method of claim 1, wherein the speech of the recordingrecognized comprises at least one of a song title, an artist name and amusic category.
 10. The method of claim 1, wherein the client stateinformation includes one or more of application ID of the musicapplication, user ID of the user, text field ID of the music applicationand current state of the music application.
 11. A method for enteringtext into a music system using a processor, comprising: recording speechpresented by a user using a resident capture facility; providing thespeech as a recording to a speech recognition facility; selecting atleast one statistical language model from a set of language models basedat least in part on contextual information relating to the recording,wherein the at least one statistical language model selected includes ageneral language model for artists, a general language model for songtitles, and a general language model for music types; determining thatthe at least one statistical language model selected providesinsufficient recognition output and requires an additional recognitionpass of the recording; conducting the additional recognition pass of therecording; selecting at least one other statistical language model basedat least in part on the additional recognition pass of the recording andclient state information of the recording; generating results utilizingthe speech recognition facility; allowing the user to alter the results;and using the results in the music system, wherein the music systemprovides information relating to a music application to the speechrecognition facility, wherein generating the results is based at leastin part on the information, wherein allowing the user to alter theresults includes the user editing a text result using at least one of aset of buttons, other controls, and a screen-based text correctionmechanism on the music system, wherein the information includes usagehistory of the music application and information from at least one of afavorites list and playlists of the user.
 12. The method of claim 11wherein the speech recognition facility is remotely located from themusic system.
 13. The method of claim 11 wherein the informationrelating to the music application running on the music system isprovided to the speech recognition facility.
 14. The method of claim 11wherein allowing the user to alter the results includes selecting fromamong a plurality of alternate choices of words contained in the resultsfrom the speech recognition facility.
 15. The method of claim 11 whereinallowing the user to alter the results includes selecting from among aplurality of alternate actions related to the results from the speechrecognition facility.
 16. The method of claim 11, further comprisingcombining words recognized from more than one set of language models togenerate the results.
 17. The method of claim 11, wherein allowing theuser to alter the results includes adapting the statistical languagemodel selected using user feedback.
 18. The method of claim 11, whereinthe speech recognized comprises at least one of a city, a state and aregion.
 19. A system for entering text into a music system comprising: aresident capture facility for recording speech presented by a user; aspeech recognition facility for receiving the speech as a recording, fordetermining that the at least one statistical language model selectedprovides insufficient recognition output and requires an additionalrecognition pass of the recording, for conducting the additionalrecognition pass of the recording, for selecting at least one otherstatistical language model based at least in part on the additionalrecognition pass of the recording and client state information of therecording, and for generating results by selecting at least onestatistical language model from a set of language models based at leastin part on contextual information relating to the recording, wherein theat least one selected statistical language model includes a generallanguage model for artists, a general language model for song titles,and a general language model for music types; and the music system forusing the results, wherein the music system provides informationrelating to a music application to the speech recognition facility andwherein the results are generated based at least in part on theinformation, and wherein the contextual information includes usagehistory of the music application and information from at least one of afavorites list and playlists of the user.
 20. The system of claim 19wherein the speech recognition facility is remotely located from themusic system.
 21. The system of claim 19 wherein the speech recognitionfacility generates the results based at least in part on the informationrelating to the music application that is received from the musicsystem.
 22. The system of claim 21 wherein the information relating tothe music application includes at least one of an identity of the musicapplication, an identity of a text box within the music application, thecontextual information within the music application, an identity of themusic system, and an identity of the user.
 23. The system of claim 19,wherein the speech recognition facility allows the user an opportunityto alter the speech from the recording recognized by the speechrecognition facility.
 24. The system of claim 19, wherein the systemgenerates the results selected from at least one of selecting an outputof the music system, adjusting a volume output of the music system andgenerating a playlist of the playlists for the music system.
 25. Thesystem of claim 19, further comprising a database of music contentaccessible to the speech recognition facility.